Data can confuse as much as it illuminates. Modelling behaviours, emotions and moments can help us identify consumers and when to reach them.
Many marketers think reaching the right audience with the right message at the right time is the holy grail of media. But this is becoming increasingly complex for global brands and campaigns that cross borders and cultures.
Lost among the myriad data points, profiling, and targeting tools now available, many brands are struggling to identify even the first of those three ‘rights’. And they aren’t helped by media’s traditional ways of working.
In a typical campaign brief, for instance, we might be asked to identify people looking for a new car, who want on-demand entertainment or are seeking an alternative to high-calorie juices. That target audience is then translated into a demographic: 18-34s, 35-44s, Male or Female, Urban, $50k+, ABC1.
The trouble is, while these kinds of demographics might have been suitable in an offline world, where selecting properties that maximised coverage of a demographic helped you reach your desired audience, they have limited use in a digital world.
Putting audiences first
That’s because the digital world thinks audience first, not media first. So grouping audiences into demographics can be counterproductive. Online, these definitions are too narrow and exclude potential consumers who don’t fit a rigid profile. We call this valuable wastage.
I recently ran a data analysis for a client, which revealed 35% of its sales came from valuable wastage – audiences outside of its broad 18-34 target. Staggeringly, 50%came from outside its 18-24 bullseye.
With so many data points, profiling, and targeting tools to choose from, some brands have lost their way
If this client had relied on demographic targeting alone, they would have massively limited their ability to build brand awareness and drive sales.
This is not a unique case study. In 2016, many brands realised that over-targeting brand loyalists (or people who look like them) was restricting their ability to reach new growth audiences.
So how do we overcome this problem? Moving from demographic targeting towards blanket audience coverage is often a step too far backward. Thankfully, there is a harmonious middle ground between coverage and targeting.
The BEM approach
At MediaCom, we analyse Behaviours, Emotions and Moments and build more optimised audience models. We call this the BEM approach. We only use demographic data to do one thing: remove outliers.
Powered by consumer data, the BEM approach helps us understand:
Behaviours: Have consumers shown interest in (or exhibited behaviour that indicates they might be interested in) a specific product vertical or related area? Have they actively sought out or mentioned a specific product or service?
Emotions: What is the general mood of a nation? Has a particular product or service suddenly become more relevant? Are consumers displaying particular emotional responses to suggest they would be more receptive to certain brand messages?
Moments: Has a trigger like weather, transportation issues or other real-world events caused a product or service to become relevant? Has the consumer entered a specific location where certain products are more easily available or can help them?
The BEM model allows us to combine new data sources and triggers – including conversation scrapes, content emotion analysis and real-world events – with programmatic buying technology to improve targeting and tailor messages for local cultures.
Overlaid on top of broader-reaching brand-building activity, we can use BEM targeting to capture consumers moving into a consideration mindset, and speak to them directly with relevant messages. The results can be dramatic.
BEM targeting enables us to capture consumers as they move into a consideration mindset, and begin a more relevant and direct conversation
BEM in action
In a recent test run for a blue-chip MediaCom client (whose advertised product is positioned around breaks and relaxation), we used the BEM model to analyse Facebook conversation data and understand consumers’ daily ‘stress’ moments.
We built customised creative responding to these moments and served it programmatically to reach consumers when they needed a break. We targeted consumers based on their behaviours and emotions (analysing positive/negative sentiment of their previous Facebook posts), and the moments that influenced them to share (like time of day, location, and weather).
The BEM approach worked. By targeting consumers in this way, rather than using demographics, we doubled brand preference and purchase intent for our client’s product when compared with typical demographic targeting. Base level activity outside of typical daily ‘stress’ times ensured that we built salience in addition to this ‘moment’ targeting.
The BEM model is often a more accurate targeting model than demographics, and far more efficient than blanket audience coverage. It increases media effectiveness and media efficiencies by turning data from filter to facilitator. Isn’t it time you considered behaviours, emotions and moments?
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